Deep Convolutional Neural Networks (Deep CNNs) are at the heart of the remarkable development in deep learning. CNNs have already been used in the 90's to solve problems of character recognition, but their use has become as widespread as it is now thanks to recent researches. These CNNs have won the 2012 ImageNet image classification tournament, crushing other competitors. Then, the convolutional neural network became a very useful tool in the field of the machine learning.
Recently, the CNNs have been widely used in autonomous driving. The CNNs can perform various functions to support the autonomous driving, such as semantic segmentation, object detection, etc., for autonomous vehicles, which are learned by using tens of thousands of training data sets, and then installed on the autonomous vehicles.
Meanwhile, when the autonomous vehicles have been used for a long time, problems may arise. One of them is that new objects that have not existed during learning are added to the road over time, so that the CNN cannot properly function as an object detector. For example, new vehicles are released every year and their designs change, in which case the object detector may not be able to detect these new vehicles as vehicles. Therefore, the CNN installed on the autonomous vehicle needs to be updated regularly. However, the learning process for updating parameters of the CNN is costly and the transferring process of the parameters and related information to each autonomous vehicle for updating is also costly.